Understanding Machine Learning Applications in Lung Transplantation: A Narrative Review.

Area of Science:

• Transplantation research
• Medical informatics
• Artificial intelligence in medicine

Background:

• Lung transplantation (LTx) is vital for end-stage lung disease but faces challenges like donor scarcity and graft failure.
• Machine learning (ML) offers potential solutions by analyzing complex data to uncover insights and enhance outcomes.

Purpose of the Study:

• To review machine learning studies in lung transplantation.
• To explain ML methodologies and their application in LTx.
• To highlight the potential and challenges of ML in improving LTx outcomes.

Main Methods:

• Review of existing literature on ML applications in LTx.
• Explanation of various ML techniques including support vector machines, deep learning, random forests, and transfer learning.
• Discussion of ML's role in analyzing multi-omics data and imaging.

Main Results:

• ML shows promise in organ allocation and predicting LTx outcomes.
• Specific ML techniques are effective for risk stratification and improving interpretability.
• Transfer learning aids model development in limited data scenarios.
• ML is increasingly used for multi-omics and imaging diagnostics.

Conclusions:

• Barriers to clinical adoption include small datasets, data inconsistency, poor interpretability, and lack of validation.
• Future progress depends on multicenter collaborations, transparent methods, and clinical workflow integration.
• ML should augment, not replace, clinical judgment to improve LTx outcomes.